Pierre Mulliez
‘As part of the course “Programming in R” at IE - HST, we foster our newly learned skills by participating in a Kaggle competition. The problem requires us to do Exploratory Data Analysis, Data Cleaning and Manipulation and implement some sort of Machine Learning in R.’
The data can be found here
We have designed several plots to understand and include the right variable in our prediction, first we took a closer look at the stations pattern depending on the dates:
We have merged the station additional information file with the summary of each stations as a data table to produce a comprehensible comparison between the average of each stations accross all date, its altitude and coordinate ### Here we are plotting each station to its location on a map with a color code corresponding to it’s altitude
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Following the analysis on their relative observation based on their location we thought we could establish some segmentation:
train_index <- sample(1:nrow(solar_data), 0.7*nrow(solar_data))
val_index <- sample(setdiff(1:nrow(solar_data), train_index), 0.15*nrow(solar_data));
test_index <- setdiff(1:nrow(solar_data), c(train_index, val_index))
‘put stuff here’
‘put stuff here’
‘put stuff here’